


"""simple docstring"""






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_lowerCAmelCase							:    Optional[int]					   =	logging.getLogger(__name__)







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Tuple:
   '''simple docstring'''





   if os.path.exists(_lowerCamelCase       ):
      if os.path.exists(os.path.join(_lowerCamelCase			,		"config.json"       )       ) and os.path.isfile(
          os.path.join(_lowerCamelCase			,		"config.json"       )       ):
         os.remove(os.path.join(_lowerCamelCase			,		"config.json"       )       )
      if os.path.exists(os.path.join(_lowerCamelCase			,		"pytorch_model.bin"       )       ) and os.path.isfile(
          os.path.join(_lowerCamelCase			,		"pytorch_model.bin"       )       ):
         os.remove(os.path.join(_lowerCamelCase			,		"pytorch_model.bin"       )       )
   else:
      os.makedirs(_lowerCamelCase       )
   model.save_pretrained(_lowerCamelCase       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     2
   if unlogit:
      _lowerCamelCase   :				List[Any]							=     torch.pow(_lowerCamelCase			,		_lowerCamelCase       )
   _lowerCamelCase   :				Dict							=     p * torch.log(_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     0
   return -plogp.sum(dim=-1       )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(_lowerCamelCase       )       )       )       )
   for row in range(len(_lowerCamelCase       )       ):
      if tensor.dtype != torch.long:
         logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data       )       )
      else:
         logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data       )       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=True			,		_lowerCamelCase=True			,		_lowerCamelCase=None			,		_lowerCamelCase=False       )    ->     Tuple:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     model.config.num_hidden_layers, model.config.num_attention_heads
   _lowerCamelCase   :				Optional[Any]							=     torch.zeros(_lowerCamelCase			,		_lowerCamelCase       ).to(args.device       )
   _lowerCamelCase   :				Tuple							=     torch.zeros(_lowerCamelCase			,		_lowerCamelCase       ).to(args.device       )

   if head_mask is None:
      _lowerCamelCase   :				Optional[int]							=     torch.ones(_lowerCamelCase			,		_lowerCamelCase       ).to(args.device       )

   head_mask.requires_grad_(requires_grad=_lowerCamelCase       )
   # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
   if actually_pruned:
      _lowerCamelCase   :				str							=     None

   _lowerCamelCase   :				str							=     0.0
   _lowerCamelCase   :				Any							=     0.0
   for step, inputs in enumerate(tqdm(_lowerCamelCase			,		desc="Iteration"			,		disable=args.local_rank not in [-1, 0]       )       ):
      _lowerCamelCase   :				List[str]							=     tuple(t.to(args.device       ) for t in inputs       )
      (_lowerCamelCase      )   :				int							=     inputs

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      _lowerCamelCase   :				List[Any]							=     model(_lowerCamelCase			,		labels=_lowerCamelCase			,		head_mask=_lowerCamelCase       )
      #  (loss), lm_logits, presents, (all hidden_states), (attentions)
      _lowerCamelCase   :				List[str]							=     (
          outputs[0],
          outputs[1],
          outputs[-1],
      )  # Loss and logits are the first, attention the last
      loss.backward()  # Backpropagate to populate the gradients in the head mask
      total_loss += loss.detach().cpu().numpy()
      if compute_entropy:
         for layer, attn in enumerate(_lowerCamelCase       ):
            _lowerCamelCase   :				int							=     entropy(attn.detach()			,		_lowerCamelCase       )
            attn_entropy[layer] += masked_entropy.sum(-1       ).sum(0       ).sum(0       ).detach()

      if compute_importance:
         head_importance += head_mask.grad.abs().detach()
      tot_tokens += torch.ones_like(_lowerCamelCase       ).float().detach().sum().data

   # Normalize
   attn_entropy /= tot_tokens
   head_importance /= tot_tokens
   # Layerwise importance normalization
   if not args.dont_normalize_importance_by_layer:
      _lowerCamelCase   :				Any							=     2
      _lowerCamelCase   :				Dict							=     torch.pow(torch.pow(_lowerCamelCase			,		_lowerCamelCase       ).sum(-1       )			,		1 / exponent       )
      head_importance /= norm_by_layer.unsqueeze(-1       ) + 1e-20

   if not args.dont_normalize_global_importance:
      _lowerCamelCase   :				Union[str, Any]							=     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

   # Print matrices
   if compute_entropy:
      logger.info("Attention entropies"       )
      print_ad_tensor(_lowerCamelCase       )
   if compute_importance:
      logger.info("Head importance scores"       )
      print_ad_tensor(_lowerCamelCase       )
   logger.info("Head ranked by importance scores"       )
   _lowerCamelCase   :				str							=     torch.zeros(head_importance.numel()			,		dtype=torch.long			,		device=args.device       )
   _lowerCamelCase   :				Optional[int]							=     torch.arange(
       head_importance.numel()			,		device=args.device       )
   _lowerCamelCase   :				Optional[Any]							=     head_ranks.view_as(_lowerCamelCase       )
   print_ad_tensor(_lowerCamelCase       )
   return attn_entropy, head_importance, total_loss







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     compute_heads_importance(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		compute_entropy=_lowerCamelCase       )
   _lowerCamelCase   :				int							=     1 / loss  # instead of downsteam score use the LM loss
   logger.info("Pruning: original score: %f, threshold: %f"			,		_lowerCamelCase			,		original_score * args.masking_threshold       )

   _lowerCamelCase   :				List[str]							=     torch.ones_like(_lowerCamelCase       )
   _lowerCamelCase   :				int							=     max(1			,		int(new_head_mask.numel() * args.masking_amount       )       )

   _lowerCamelCase   :				Dict							=     original_score
   while current_score >= original_score * args.masking_threshold:
      _lowerCamelCase   :				Any							=     new_head_mask.clone().detach()  # save current head mask
      # heads from least important to most - keep only not-masked heads
      _lowerCamelCase   :				Tuple							=     float("Inf"       )
      _lowerCamelCase   :				Dict							=     head_importance.view(-1       ).sort()[1]

      if len(_lowerCamelCase       ) <= num_to_mask:
         print("BREAK BY num_to_mask"       )
         break

      # mask heads
      _lowerCamelCase   :				List[str]							=     current_heads_to_mask[:num_to_mask]
      logger.info("Heads to mask: %s"			,		str(current_heads_to_mask.tolist()       )       )
      _lowerCamelCase   :				Tuple							=     new_head_mask.view(-1       )
      _lowerCamelCase   :				str							=     0.0
      _lowerCamelCase   :				Any							=     new_head_mask.view_as(_lowerCamelCase       )
      _lowerCamelCase   :				List[str]							=     new_head_mask.clone().detach()
      print_ad_tensor(_lowerCamelCase       )

      # Compute metric and head importance again
      _lowerCamelCase   :				Dict							=     compute_heads_importance(
          _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		compute_entropy=_lowerCamelCase			,		head_mask=_lowerCamelCase       )
      _lowerCamelCase   :				Tuple							=     1 / loss
      logger.info(
          "Masking: current score: %f, remaining heads %d (%.1f percents)"			,		_lowerCamelCase			,		new_head_mask.sum()			,		new_head_mask.sum() / new_head_mask.numel() * 100			,		)

   logger.info("Final head mask"       )
   print_ad_tensor(_lowerCamelCase       )
   np.save(os.path.join(args.output_dir			,		"head_mask.npy"       )			,		head_mask.detach().cpu().numpy()       )

   return head_mask







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     datetime.now()
   _lowerCamelCase   :				List[Any]							=     compute_heads_importance(
       _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		compute_entropy=_lowerCamelCase			,		compute_importance=_lowerCamelCase			,		head_mask=_lowerCamelCase       )
   _lowerCamelCase   :				Any							=     1 / loss
   _lowerCamelCase   :				Union[str, Any]							=     datetime.now() - before_time

   _lowerCamelCase   :				Any							=     sum(p.numel() for p in model.parameters()       )
   _lowerCamelCase   :				int							=     {
       layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowerCamelCase       )       )
   }

   for k, v in heads_to_prune.items():
      if isinstance(_lowerCamelCase			,		_lowerCamelCase       ):
         _lowerCamelCase   :				Dict							=     [
             v,
         ]

   assert sum(len(_lowerCamelCase       ) for h in heads_to_prune.values()       ) == (1 - head_mask.long()).sum().item()
   model.prune_heads(_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     sum(p.numel() for p in model.parameters()       )

   _lowerCamelCase   :				Any							=     datetime.now()
   _lowerCamelCase   :				List[Any]							=     compute_heads_importance(
       _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		compute_entropy=_lowerCamelCase			,		compute_importance=_lowerCamelCase			,		head_mask=_lowerCamelCase			,		actually_pruned=_lowerCamelCase			,		)

   _lowerCamelCase   :				Optional[int]							=     1 / loss
   _lowerCamelCase   :				Any							=     datetime.now() - before_time

   logger.info(
       "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"			,		_lowerCamelCase			,		_lowerCamelCase			,		pruned_num_params / original_num_params * 100			,		)
   logger.info("Pruning: score with masking: %f score with pruning: %f"			,		_lowerCamelCase			,		_lowerCamelCase       )
   logger.info("Pruning: speed ratio (original timing / new timing): %f percents"			,		original_time / new_time * 100       )
   save_model(_lowerCamelCase			,		args.output_dir       )







def        lowerCamelCase_(		)    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--data_dir"			,		default=_lowerCamelCase			,		type=_lowerCamelCase			,		required=_lowerCamelCase			,		help="The input data dir. Should contain the .tsv files (or other data files) for the task."			,		)
   parser.add_argument(
       "--model_name_or_path"			,		default=_lowerCamelCase			,		type=_lowerCamelCase			,		required=_lowerCamelCase			,		help="Path to pretrained model or model identifier from huggingface.co/models"			,		)
   parser.add_argument(
       "--output_dir"			,		default=_lowerCamelCase			,		type=_lowerCamelCase			,		required=_lowerCamelCase			,		help="The output directory where the model predictions and checkpoints will be written."			,		)

   # Other parameters
   parser.add_argument(
       "--config_name"			,		default=""			,		type=_lowerCamelCase			,		help="Pretrained config name or path if not the same as model_name_or_path"			,		)
   parser.add_argument(
       "--tokenizer_name"			,		default=""			,		type=_lowerCamelCase			,		help="Pretrained tokenizer name or path if not the same as model_name_or_path"			,		)
   parser.add_argument(
       "--cache_dir"			,		default=_lowerCamelCase			,		type=_lowerCamelCase			,		help="Where do you want to store the pre-trained models downloaded from s3"			,		)
   parser.add_argument(
       "--data_subset"			,		type=_lowerCamelCase			,		default=-1			,		help="If > 0: limit the data to a subset of data_subset instances."       )
   parser.add_argument(
       "--overwrite_output_dir"			,		action="store_true"			,		help="Whether to overwrite data in output directory"       )
   parser.add_argument(
       "--overwrite_cache"			,		action="store_true"			,		help="Overwrite the cached training and evaluation sets"       )

   parser.add_argument(
       "--dont_normalize_importance_by_layer"			,		action="store_true"			,		help="Don\'t normalize importance score by layers"       )
   parser.add_argument(
       "--dont_normalize_global_importance"			,		action="store_true"			,		help="Don\'t normalize all importance scores between 0 and 1"			,		)

   parser.add_argument(
       "--try_masking"			,		action="store_true"			,		help="Whether to try to mask head until a threshold of accuracy."       )
   parser.add_argument(
       "--masking_threshold"			,		default=0.9			,		type=_lowerCamelCase			,		help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."			,		)
   parser.add_argument(
       "--masking_amount"			,		default=0.1			,		type=_lowerCamelCase			,		help="Amount to heads to masking at each masking step."       )
   parser.add_argument("--metric_name"			,		default="acc"			,		type=_lowerCamelCase			,		help="Metric to use for head masking."       )

   parser.add_argument(
       "--max_seq_length"			,		default=128			,		type=_lowerCamelCase			,		help=(
           "The maximum total input sequence length after WordPiece tokenization. \n"
           "Sequences longer than this will be truncated, sequences shorter padded."
       )			,		)
   parser.add_argument("--batch_size"			,		default=1			,		type=_lowerCamelCase			,		help="Batch size."       )

   parser.add_argument("--seed"			,		type=_lowerCamelCase			,		default=42       )
   parser.add_argument("--local_rank"			,		type=_lowerCamelCase			,		default=-1			,		help="local_rank for distributed training on gpus"       )
   parser.add_argument("--no_cuda"			,		action="store_true"			,		help="Whether not to use CUDA when available"       )
   parser.add_argument("--server_ip"			,		type=_lowerCamelCase			,		default=""			,		help="Can be used for distant debugging."       )
   parser.add_argument("--server_port"			,		type=_lowerCamelCase			,		default=""			,		help="Can be used for distant debugging."       )
   _lowerCamelCase   :				Tuple							=     parser.parse_args()

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print("Waiting for debugger attach"       )
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,		redirect_output=_lowerCamelCase       )
      ptvsd.wait_for_attach()

   # Setup devices and distributed training
   if args.local_rank == -1 or args.no_cuda:
      _lowerCamelCase   :				str							=     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
      _lowerCamelCase   :				Optional[Any]							=     0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank       )
      _lowerCamelCase   :				Dict							=     torch.device("cuda"			,		args.local_rank       )
      _lowerCamelCase   :				Optional[Any]							=     1
      torch.distributed.init_process_group(backend="nccl"       )  # Initializes the distributed backend

   # Setup logging
   logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN       )
   logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device			,		args.n_gpu			,		bool(args.local_rank != -1       )       )       )

   _lowerCamelCase   :				List[Any]							=     GPTaLMHeadModel.from_pretrained(args.model_name_or_path       )

   # Distributed and parallel training
   model.to(args.device       )
   if args.local_rank != -1:
      _lowerCamelCase   :				Tuple							=     nn.parallel.DistributedDataParallel(
          _lowerCamelCase			,		device_ids=[args.local_rank]			,		output_device=args.local_rank			,		find_unused_parameters=_lowerCamelCase       )
   elif args.n_gpu > 1:
      _lowerCamelCase   :				Optional[Any]							=     nn.DataParallel(_lowerCamelCase       )

   # Print/save training arguments
   os.makedirs(args.output_dir			,		exist_ok=_lowerCamelCase       )
   torch.save(_lowerCamelCase			,		os.path.join(args.output_dir			,		"run_args.bin"       )       )
   logger.info("Training/evaluation parameters %s"			,		_lowerCamelCase       )

   # Prepare dataset
   _lowerCamelCase   :				Any							=     np.concatenate(
       [
           np.loadtxt(args.data_dir			,		dtype=np.intaa       ),
       ]       )
   _lowerCamelCase   :				int							=     (torch.from_numpy(_lowerCamelCase       ),)
   _lowerCamelCase   :				int							=     TensorDataset(*_lowerCamelCase       )
   _lowerCamelCase   :				List[str]							=     RandomSampler(_lowerCamelCase       )
   _lowerCamelCase   :				List[str]							=     DataLoader(_lowerCamelCase			,		sampler=_lowerCamelCase			,		batch_size=args.batch_size       )

   # Compute head entropy and importance score
   compute_heads_importance(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # Try head masking (set heads to zero until the score goes under a threshole)
   # and head pruning (remove masked heads and see the effect on the network)
   if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
      _lowerCamelCase   :				int							=     mask_heads(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )
      prune_heads(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )


if __name__ == "__main__":
  main()







'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


